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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 775 Documents
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Engineering Resilient Edge Traffic Infrastructure for Internet-Scale Distributed Systems Sanjay Singh
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.2055

Abstract

Edge traffic infrastructure has evolved from a packet-forwarding function into a critical decision-making layer that influences application availability, security, and user experience across globally distributed platforms. This article examines the architectural principles required to achieve resilience in edge traffic infrastructure operating at internet scale. The approach synthesizes established practices in distributed systems, load balancing, service discovery, content delivery, transport protocols, observability, reliability engineering, security, progressive delivery, and chaos engineering to identify their combined contribution to resilient edge operations. The analysis indicates that resilience depends on the coordinated application of multiple architectural principles rather than on a single control mechanism. Stateless traffic handling supports horizontal scalability and reduces operational dependence on session affinity, while centralized and version-controlled configuration management limits regional policy drift and improves consistency. Continuous observability, automated health monitoring, and failure-response mechanisms enable earlier detection and mitigation of infrastructure anomalies. Geographic traffic distribution and content delivery mechanisms further improve availability and performance by reducing latency and distributing demand across geographically separated resources. The analysis also shows that reliability and security increasingly converge at the edge because failures, attacks, and configuration errors can propagate across shared infrastructure and affect large user populations. Emerging approaches, including predictive traffic engineering, autonomous control, identity-aware networking, and unified networking, security, and observability platforms, are extending conventional traffic-management architectures toward more adaptive operational models. The findings establish edge traffic engineering as a distinct architectural discipline characterized by request-level sensitivity, geographic distribution, continuous control, and broad organizational impact. Resilient edge infrastructure, therefore, requires integrated architectural, operational, and security mechanisms capable of maintaining service continuity under changing traffic conditions and partial system failures
Web-based Direct-to-Consumer Marketplace for Small-Scale Fisheries Products: Rapid Application Development and Functional Evaluation in Jaring Halus Village, Langkat, Indonesia Supiyandi Supiyandi; Ramlah binti Mailok; Herdianto Herdianto
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1897

Abstract

Fishery and micro-enterprise products in Jaring Halus Village, Langkat Regency, Indonesia, are sold primarily within the village through face-to-face transactions and manual record keeping. This condition limits producers’ market reach to physically present consumers and provides no persistent transaction data for monitoring or business development. Previous studies on digital technology in small-scale fisheries have focused largely on catch recording and traceability, while applied marketplace development studies rarely describe their evaluation scope with sufficient precision to support replication. This study designs, develops, and functionally evaluates a web-based direct-to-consumer marketplace to address this gap. The system was developed using the Rapid Application Development model, with requirements elicited from fishers, sellers, and prospective buyers through interviews and observations, followed by public deployment. The marketplace integrates account management, product catalogue administration with stock and discount attributes, shopping cart and order management, delegated electronic payment settlement, and content management. Functional performance was evaluated using black-box testing across eight interface areas involving seventeen test cases. All test cases produced results consistent with their specified requirements. Partition analysis showed that eleven test cases exercised valid input classes, while six addressed invalid input classes. Four interface areas contained no invalid-input test cases, and the content-management module was not covered by the testing process. The study contributes a transferable reference architecture for direct-to-consumer fisheries marketplaces in low-connectivity rural environments, a traceable requirement-to-test structure, and prioritized recommendations based on identified testing gaps. The evidence is limited to functional conformance; therefore, usability, technology acceptance, user satisfaction, and economic outcomes were not measured and are not claimed. The findings provide a foundation for subsequent evaluation and iterative improvement of digital marketplaces for rural fishery micro-enterprises
Physical AI in Heavy Equipment: A Review of Perception, Safety, Simulation, and Validation Architectures for Autonomous Off-Highway Machines Shyam Laheri Chunduri
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.2037

Abstract

Off-highway equipment is undergoing a transition toward autonomous and AI-enabled operation under demanding duty cycles, limited connectivity, environmental variability, and persistent labor shortages. This review examines the engineering requirements for deploying physical AI in heavy equipment, with particular emphasis on perception, functional safety, validation, and autonomous operation. The review synthesizes recent literature published from 2020 onward across five interconnected domains: multi-sensor perception and scene understanding; functional safety and AI-related standards; simulation and digital twin infrastructure; autonomous haulage in surface and underground mining; and autonomous navigation in agricultural machinery. The analysis identifies a growing gap between the capabilities of field-oriented perception and control architectures and the validation provisions required to demonstrate safe and reliable AI behavior. Across the reviewed domains, simulation, digital twins, model-in-the-loop, software-in-the-loop, and hardware-in-the-loop testing increasingly constitute essential infrastructure for evaluating safety-relevant AI behavior before field deployment. The review further indicates that validation capacity is becoming a critical engineering constraint for scaling physical AI, particularly when perception uncertainty, environmental variability, sensor degradation, communication limitations, and hardware–software interactions must be assessed under representative operating conditions. These findings show that autonomous capability development alone is insufficient to support operational deployment without a corresponding validation architecture. The review therefore proposes a validation-centric sequencing framework that connects perception development, simulation, model- and software-in-the-loop testing, hardware-in-the-loop verification, safety assurance, and field validation. The framework provides an engineering basis for organizing progressive validation activities, establishing traceability between development and verification, and reducing the gap between AI capability development and reliable operational deployment of autonomous off-highway equipment
A Digital Ergonomics Framework for Preventive Workstation and Work-Process Optimization Using OWAS-Based Risk Assessment Pande Ketut Sudiarta; I Ketut Eddy Purnama; I Putu Gede Adiatmika
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1923

Abstract

Ergonomic workstation design is essential for improving the fit between workers, tasks, and the work environment while reducing the risk of work-related musculoskeletal disorders. The Ovako Working Posture Analysis System (OWAS) is widely used for ergonomic risk assessment; however, its conventional application to dynamic work can be inefficient because numerous working postures must be observed and evaluated. Moreover, conventional ergonomic design often requires multiple physical mock-ups, while motion-capture technologies such as Kinect are generally applied to existing work conditions, limiting their use for preventive evaluation during the design stage. This study proposes a digital ergonomics framework integrating workstation modeling, work-process simulation, and OWAS-based risk assessment for preventive ergonomic optimization. Digital models of the workstation and work activities were developed to simulate worker postures and identify potentially hazardous conditions before physical implementation. OWAS was used to classify postural risks and determine the required corrective actions. Workstation configurations and work procedures associated with higher risk levels were iteratively modified and reassessed in the digital environment. The results demonstrate that the proposed framework can identify and reduce ergonomic risks during the design stage without extensive physical prototyping. Higher-risk postures were mitigated through iterative modifications of workstation configurations and work procedures. This framework provides a systematic preventive approach for integrating ergonomic assessment into workstation and work-process design, supporting safer working conditions while improving the efficiency of the ergonomic design process
Enhancing Personalized Digital Experiences Through Adobe Experience Manager, Data Integration, and Artificial Intelligence Sham Sunder Reddy Dadi
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.2025

Abstract

Personalized digital experiences have become a standard expectation for enterprise customers, yet personalization initiatives face a persistent tension between increasing contextual relevance and protecting customer trust. This paper develops a governance-aware personalization architecture that integrates Adobe Experience Manager with secure customer data management and artificial intelligence-driven recommendations. The work aims to establish an architectural approach in which personalization capabilities are constrained by privacy, transparency, accountability, and auditability requirements rather than implemented as independent controls. The approach synthesizes established principles of recommender systems, privacy-aware personalization, responsible artificial intelligence, structured content management, and customer-data governance into an integrated architecture. Key mechanisms include consent-scoped data segmentation, graduated personalization, structured content delivery, AI-based recommendation using permitted customer attributes, visible recommendation rationale, and governance-event logging. The resulting architecture establishes explicit relationships between data authorization, personalization intensity, recommendation inputs, content selection, and audit records. The authorized consent scope constrains the intensity of personalization, while recommendation processes are limited to permitted data inputs and provide interpretable rationales for the generated recommendations. Governance-relevant events are also recorded to support traceability, review, and accountability throughout the personalization lifecycle. The results of the design analysis indicate that privacy and personalization requirements can be incorporated into the same architectural flow rather than treated as competing or isolated functions. The architecture provides a structured basis for delivering contextually relevant digital experiences while maintaining governance constraints over customer data and automated recommendations. The findings support the use of governance-aware architecture as a practical design approach for enterprise personalization systems. However, empirical validation is still required to determine its effects on customer trust, conversion outcomes, recommendation accuracy, and the trade-offs between personalization intensity and privacy protection
Kubernetes-Native Orchestration for Real-Time Machine Learning Inferencing Hemanth Kumar Padakanti
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.2061

Abstract

Machine learning inference requires serving infrastructure that can maintain responsiveness, reliability, and prediction quality under variable request loads. Conventional Kubernetes orchestration does not fully address inference-specific characteristics, including multi-minute pod startup times, non-oversubscribable accelerators, highly variable per-request computational costs, and prediction-quality degradation that can occur without infrastructure failures. This article develops a Kubernetes-native architecture for reliable and responsive model serving by examining these characteristics and mapping them to dedicated infrastructure mechanisms. The approach integrates queue- or concurrency-based autoscaling, topology-aware GPU scheduling, warm-capacity buffering, readiness gating based on completed model warmup, request batching, independent infrastructure and model observability, and controlled rollout strategies. The resulting architecture establishes separate operational controls for infrastructure responsiveness and prediction-quality assurance, reducing dependence on CPU utilization as the primary scaling indicator. The analysis also identifies operational constraints for models that exceed single-device memory, workloads requiring shared GPU cost attribution, and services dominated by unavoidable cold-start latency. These findings indicate that effective inference infrastructure requires orchestration policies tailored to the computational and operational properties of model serving rather than generic container workloads. The proposed architecture provides a structured basis for deploying scalable inference services while explicitly accounting for startup latency, accelerator constraints, workload variability, and silent prediction degradation. Its applicability remains bounded by model size, accelerator topology, workload characteristics, and the economic requirements of the deployment environment. The architecture, therefore, supports a more explicit alignment between Kubernetes orchestration decisions and the distinctive resource, performance, and reliability requirements of machine learning inference across production environments with heterogeneous models, accelerators, and service-level performance requirements and deployment conditions
When Does the Preferred Supply-Chain Configuration Change? A Switching-Boundary Decision Analysis of a Smallholder Rubber Case Muhammad Izman Herdiansyah; Bosya Perdana; Sunda Ariana
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1988

Abstract

Static supply-chain optimization identifies a preferred configuration under fixed conditions but does not indicate when that recommendation should be reconsidered as modeled conditions deteriorate. This study develops a Switching-Boundary Decision Analysis (SBDA) layer over three representative linear-programming (LP) instances previously reported for smallholder rubber marketing channels in Musi Rawas, South Sumatra, Indonesia. The published instances were independently reproduced and evaluated through 82 predefined deterministic scenario states across three configurations, yielding 246 LP solves. Stress tests examined configuration-specific and system-wide changes in modeled distribution-profit coefficients, transportation capacity, labor/time capacity, product coefficients, and compound conditions, supplemented by an equal-throughput benchmark. Baseline reproduction confirmed C3, the UPPB-based configuration, as preferred under native-capacity modeled profit, at approximately Rp5.567 million. A C3-specific deterioration of approximately 7.74% produced the first C3-to-C1 crossover under native-capacity profit comparison, whereas the unit-normalized crossover occurred at approximately 16.96%. Under labor/time deterioration, the C3 labor/time constraint became binding at approximately 41.41%, while preference switching occurred later, at approximately 45.94%. Stress testing also identified meaningful optimal-allocation changes: a 50% labor/time-capacity reduction in C2 changed the solution from all-A to a mixed 230-A/300-B allocation, while equal-throughput normalization changed C3 to a mixed allocation but preserved the C3C1C2 ranking. SBDA therefore distinguishes performance deterioration, constraint activation, solution-structure change, and configuration switching, and connects these states to a parameterized Stable-Watch-Re-evaluate decision layer. The numerical boundaries are conditional on reconstructed historical instances and should not be interpreted as population-level or current-market thresholds. They provide analytical decision points rather than universal prescriptions for supply-chain management in practice
Assessing Digital Resilience in Primary Health Care: A Structural Framework Siti Nurhayati; Devi Octaviana; Hafiidhaturrahma Hafiidhaturrahma; Mochammad Agri Triansyah; Nurina Mayasari
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.2027

Abstract

Digital transformation has significantly changed healthcare delivery, particularly in primary care settings such as Puskesmas (Community Health Centers) in Indonesia. Although digital technologies offer opportunities to improve accessibility, efficiency, and service quality, the capacity of healthcare institutions to adapt to digital disruptions remains insufficiently understood. This study aims to examine digital resilience in primary healthcare through a systematic literature review, focusing on research trends, key influencing factors, thematic developments, and knowledge gaps relevant to Puskesmas. The review analyzes scholarly articles, conference papers, and other academic publications retrieved from Scopus, PubMed, and Google Scholar. The analysis considers publication trends, influential authors, geographical distribution, and major research themes concerning digital resilience in healthcare. The findings indicate growing research interest in digital resilience, particularly regarding the challenges faced by healthcare systems operating under resource constraints. Four major factors emerge as relevant to digital adaptation: technological infrastructure, digital literacy, policy support, and community engagement. However, existing research predominantly emphasizes the technical dimensions of digital technologies, while organizational capacity and sociocultural factors receive comparatively limited attention. Research addressing digital resilience in Southeast Asia, particularly in Indonesian primary healthcare settings, also remains underrepresented. These gaps limit the contextual understanding of how digital resilience develops within community-based healthcare institutions. The review concludes that strengthening digital resilience in Puskesmas requires an integrated approach that combines technological readiness, organizational preparedness, workforce development, and community participation. Future research should investigate the organizational and sociocultural dimensions of digital adaptation and develop context-specific strategies for improving digital literacy among healthcare workers and communities. Policymakers should also prioritize sustainable digital infrastructure and continuous training to support resilient, accessible, and effective primary healthcare services in Indonesia
Transformer-Based Automatic Speech Recognition Development for Transcription and Clinical Information Extraction in Indonesian on Electronic Health Records Bachtiyar Wahab; Fitriana Harahap; Khairul Anwar; Nabila Mahyuda; Nur Asiah Hasibuan
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1938

Abstract

Integrating Automatic Speech Recognition (ASR) into Electronic Health Records (EHR) has the potential to boost the efficiency of clinical documentation by automating the transcription of doctor-patient conversations. However, high-quality transcription doesn't necessarily mean the clinical information will be classified accurately. This study aims to analyze the quality of ASR transcriptions and compare the performance of Machine Learning algorithms in classifying clinical units based on Indonesian clinical texts. The dataset used consists of 1,000 medical records distributed across five clinical units: Cardiology, ENT, General, Internal Medicine, and Pediatrics. Transcription quality was evaluated using Word Error Rate (WER), while classification was done through Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction and four algorithms: Random Forest, Logistic Regression, Decision Tree, and Naive Bayes. Model evaluation was carried out using accuracy, precision, recall, and F1-score on 200 test data. The research results show that the average WER is 0.40%, indicating very good ASR transcription quality. Random Forest achieved the highest accuracy at 44.50%, Logistic Regression 42.00%, Decision Tree 38.00%, and Naive Bayes 32.00%. Random Forest performed best in the Internal Medicine Clinic with a precision of 0.58 and an F1-score of 0.53. These findings suggest that the main challenge lies in the representation of textual features, mainly due to the similarity of medical vocabulary across clinical units. This study highlights the need to develop approaches based on context, clinical entities, ICD-10 codes, and Transformers to improve classification accuracy and support the automation of clinical information management in EHRs. This approach is expected to strengthen semantic understanding, reduce classification errors, and improve system reliability as a supporter of digital clinical documentation in healthcare settings
Zero Service Desk: Enabling Autonomous IT Through AI-Driven Self-Service Anu Arora
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.2033

Abstract

Enterprise IT service management faces increasing pressure from growing infrastructure complexity, expanding service demands, and user expectations for rapid resolution. This study develops and evaluates the Zero Service Desk framework, a three-pillar model to advance autonomous IT operations by integrating AIOps, conversational artificial intelligence, and intelligent orchestration on the ServiceNow Now Platform. The framework structures autonomous service capabilities into three interconnected pillars: Prevent, Deflect, and Automate. Prevent applies infrastructure observability and machine learning-based event correlation to identify emerging anomalies and address potential incidents before they affect users. Deflect combines natural language processing, virtual agents, knowledge-based assistance, and self-service workflows to resolve user requests without human escalation. Automate employs orchestration and self-healing workflows to execute closed-loop infrastructure remediation with minimal human intervention. These capabilities are coordinated through a MAPE-K feedback architecture that enables continuous monitoring, analysis, planning, execution, and knowledge refinement across operational processes. The framework was implemented through an 18-month, three-phase roadmap consisting of CMDB alignment and observability, AIOps event management and virtual agent deployment, and autonomous remediation with continuous learning. Evaluation of the implementation demonstrated a 42% L0/L1 ticket deflection rate, a 47% reduction in mean time to resolve, 51% automation coverage, and a 60% reduction in human agent workload. SLA compliance increased from 94% to 99%. The findings demonstrate that phased integration of observability, conversational AI, and intelligent orchestration can reduce dependence on human intervention while improving service resolution performance, automation coverage, and operational reliability. The Zero Service Desk framework, therefore, provides a structured pathway toward progressively autonomous enterprise IT service management

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